Systems and methods for using eye movements to determine traumatic brain injury

ABSTRACT

Systems and methods for detecting a traumatic brain injury (TBI). The system comprises a sensing arrangement and a control unit. The sensing arrangement collects eye movement data of a user. The control unit is in communication with the sensing arrangement and configured to compare the eye movement data to one or more baseline measurements of eye movement dynamics. The control unit is also configured to generate an alert indicating the presence or severity of the TBI for delivery to control unit administrator if the eye movement data diverges from one or more of the baseline measurements by a threshold amount.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/264,131, filed Jan. 31, 2019, which is a continuation of U.S. patentapplication Ser. No. 15/504,166, filed Feb. 15, 2017, now U.S. Pat. No.10,231,617, which represents the U.S. National Stage of InternationalApplication No. PCT/US2015/046117, filed Aug. 20, 2015, which is basedon and claims the benefit of U.S. Provisional Patent Application Ser.No. 62/040,166, filed on Aug. 21, 2014, the contents of which areincorporated herein by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

N/A

BACKGROUND OF THE INVENTION

The present disclosure generally relates to systems and methods foracquiring data from a subject and, more particularly, to systems andmethods for gathering and analyzing information about the subject's eyemovements to determine or predict a state of the subject, includingconditions such as traumatic brain injury (TBI) and other neurologicalinjuries and diseases.

Brain injury can affect motor and cognitive function in the injuredsubject, and may increase the subject's vulnerability to a subsequentbrain injury. When the brain injury is caused by trauma, such as animpact or piercing of the head, the subject is many times more likely tosuffer a more severe injury the next time a similar trauma occurs.Concussion and other TBIs are currently at the forefront of sportsmedicine discussions, particularly for contact sports, because the riskto players is significant and the presence of a TBI cannot always bequickly diagnosed. For example, American football players are constantlyat risk of a concussion, but often return to the game after a TBIbecause their visible symptoms were not cause for concern and a quickobjective test is not available.

Another problem with diagnosing TBI is that most symptoms can betransient. Thus, with the passage of time it becomes more difficult todetect an injury, and medical examinations and accident investigationscan be compromised. Early, quick, and objective detection of thephysiological effects of TBI is needed.

The eye movements of people with neurological disease differsignificantly from those of healthy people. The eyes in both populationsdo not stay perfectly still during visual fixation. Fixational eyemovements and saccadic intrusions continuously change the position ofthe gaze. Microsaccades are rapid, small-magnitude involuntary saccadesthat occur several times each second during fixation; microsaccadescounteract visual fading and generate strong neural transients in theearly visual system. Microsaccades may also drive perceptual flips inbinocular rivalry. Microsaccade rates and directions are moreovermodulated by attention, and thus generate rich spatio-temporal dynamics.Further, fixational eye movements as a whole enhance fine spatialacuity. Abnormalities and intrusions in these eye movements can belieneurological impairments.

It would be beneficial to be able to detect TBI and differentiallydiagnose it from another neurological injury or disease in anon-invasive manner. The following disclosure provides one suchdifferential diagnostic method.

SUMMARY OF THE INVENTION

The present invention overcomes drawbacks of previous technologies byproviding systems and methods that afford a number of advantages andcapabilities not contemplated by, recognized in, or possible intraditional system or known methodologies related to tracking ordetermining a subject's state, including the detection of traumaticbrain injury (TBI) and other neurological injuries and diseases.

In one embodiment of the present invention, systems and methods areprovided for monitoring, recording, and/or analyzing eye movements insitu to determine whether oculomotor dynamics are being affected by theonset or presence of TBI. Eye saccades and the velocity of intersaccadiceye drift are detectably affected by the onset or presence of theseconditions. A system and method may alert a user to the presence ofthese states or conditions in a testing environment. In particular, asystem in accordance with the present invention may include devices anddevice assemblies that record baseline data of a subject and generate adata model representing the eye movement data of the subject, andfurther the system may include device and device assemblies that recordeye movement data in situ and compare it to the data model to determineif the user is affected by TBI. In one aspect, a sensor arrangement mayinclude a camera and recording assembly for detecting and recording theeye movements.

In a contemplated embodiment of the present invention, a system includesa sensing arrangement that collects eye movement data of a user, and acontrol unit in communication with the sensing arrangement. The controlunit may be configured to compare the eye movement data to one or morebaseline measurements of eye movement dynamics and, if the eye movementdata diverges from one or more of the baseline measurements by athreshold amount, generate an alert indicating the presence or severityof a TBI for delivery to the user. Comparing the eye movement data tothe baseline measurements may include calculating a currentintersaccadic drift velocity of the user and comparing the currentintersaccadic drift velocity to one or more threshold drift velocitiesof the baseline measurements. The eye movement data may include one ormore saccade parameters, and comparing the eye movement data to thebaseline measurements may include calculating a current intersaccadicdrift velocity of the user from the saccade parameters and comparing thecurrent intersaccadic drift velocity to one or more threshold driftvelocities of the baseline measurements.

In another embodiment of the present invention, a method of determininga TBI of a user includes recording from the user eye movement data ofone or both of the user's eyes, comparing the eye movement data to oneor more baseline measurements, and, if the eye movement data divergesfrom one or more of the baseline measurements by a threshold amount,delivering an alert indicating the presence or severity of a TBI to theuser. The eye movement data may include one or both of saccadeparameters and intersaccadic drift parameters.

In another embodiment of the present invention, systems and methods ofthe present invention may be combined as a kit or apparatus, whoseadvantages and capabilities will be readily apparent from descriptionsbelow.

The foregoing and other advantages of the invention will appear from thefollowing description. In the description, reference is made to theaccompanying drawings which form a part hereof, and in which there isshown by way of illustration a preferred embodiment of the invention.Such embodiment does not necessarily represent the full scope of theinvention, however, and reference is made therefore to the claims andherein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying drawings, wherein like reference numerals denote likeelements.

FIG. 1 is a diagram of a detection system in accordance with the presentinvention.

FIG. 2 is a flowchart illustrating a method for detecting traumaticbrain injury in accordance with the present invention.

DETAILED DESCRIPTION

The systems and methods for detecting onset, presence, and progressionof particular states, including traumatic brain injury (TBI), throughobservation of eye movements described herein. TBI is shown by theinventors to affect oculomotor dynamics, including saccadic metrics andintersaccadic drift metrics, with increasing severity as the injuryprogresses. In particular, intersaccadic drift velocity increases as TBIdevelops and progresses, and select oculomotor dynamics can be trackedagainst a baseline to alert a subject before the effects of TBI impairthe subject's ability to perform certain actions, such as operating amotor vehicle.

The systems and methods described herein are offered for illustrativepurposes only, and are not intended to limit the scope of the presentinvention in any way. Indeed, various modifications of the invention inaddition to those shown and described herein will become apparent tothose skilled in the art from the foregoing description and thefollowing examples and fall within the scope of the appended claims. Forexample, specific disclosure related to the detection of TBI isprovided, although it will be appreciated that the systems and methodsmay be applied for detection of any neurological injury or disease andfor any subject without undue experimentation.

Using the approach of the present invention, a detection system mayrecord eye movement data from a user, compare the eye movement data to adata model comprising threshold eye movement data samples, and from thecomparison make a determination whether or not the user's brain functionis suffering from or is subject to impairment by TBI. The detectionsystem may alert the user or another party to take corrective action ifonset or presence of a dangerous impaired condition is detected.

Referring to FIG. 1 , an embodiment of the detection system 10 mayinclude a sensing arrangement 12 configured to detect and record eyemovement dynamics of the user. The sensing arrangement 12 may includeone or more sensors suitable for collecting the eye movement data. Suchsensors may include a camera or other imaging or motion tracking devicecapable of recording at a suitably high speed and level of detail sothat the user's eye movement dynamics, including saccades andintersaccadic drift, are captured. A monocular arrangement of one ormore sensors for one of the user's eyes may be used, or one or moresensors may be included for each eye to obtain binocular data. In someembodiments, the sensors may be miniaturized or otherwise compact,portable, and non-invasive. The sensors may further bevehicle-independent, and may be wireless, to facilitate integration ofthe sensors into any deployment of the detection system 10. For example,the sensing arrangement 12 may include sensors that are integrated intoeyewear, such as on the frame or within the lenses of a pair of glasses.This allows for eye movement data collected even as the user turns hishead, and allows the sensors to be positioned close to the eyes. Inanother example, the sensors may be integrated into a heads-up displayfor a vehicle. In another example, the sensors may be integrated into ahandheld scanning device.

The sensing arrangement 12 may further include integrated or discretedevices for processing, storing, and transmitting collected data. Suchdevices may include a processor, volatile and/or permanent memory, awired or wireless transmitter, and associated power circuits and powersupply for operating the devices. Software modules may define andexecute instructions for operating the sensors, configuring databases,registers, or other data stores, and controlling transmission of thedata. The collected data may be shared via transmission to a controlunit 14 that may be integrated with or disposed physically remotely fromthe sensing arrangement 12. The eye movement data, or a subset thereof,may be transmitted in real-time as it is captured by the sensors, or itmay be stored for later transmission.

The control unit 14 may use the processing hardware (i.e., processor,memory, and the like) of the sensing arrangement 12, or may include itsown processing hardware for analyzing the eye movement data andgenerating an alert to the user if needed. The control unit 14 mayinclude a plurality of modules that cooperate to process the eyemovement data in a particular fashion, such as according to the methodsdescribed below. Each module may include software (or firmware) that,when executed, configures the control unit 14 to perform a desiredfunction. A data analysis module 16 may extract information from the eyemovement data for comparison to the data model. The data analysis module16 may include one or more data filters, such as a Butterworth or othersuitable bandpass filter, that retain only desired signal elements ofthe eye movement data. The data analysis module 16 may include programinstructions for calculating, from the eye movement data, one or moreeye movement dynamics, such as saccades and/or intersaccadic driftvelocities, of the user's eyes. The calculation may be performedsubstantially in real-time, such that a calculated intersaccadic driftvelocity may be considered the current drift velocity of the user'seyes.

A comparison module 18 may receive the processed eye movement data fromthe data analysis module 16 and may compare it to the data model asdescribed in detail below. The control unit 14 may include or haveaccess to a model data store 20 that stores the data model. The modeldata store 20 may be a database, data record, register, or othersuitable arrangement for storing data. In some embodiments, the datamodel may simply be a threshold drift velocity, and may thus be storedas a single data record in memory accessible by the comparison module18. In other embodiments, the data model may be a lookup table, linkedlist, array, or other suitable data type depending on the data samplesfor eye movement dynamics needed to be stored in the data model.

In some embodiments, the control unit 14 may include a data modelgenerator 22. The data model generator 22 is a module that receives eyemovement data collected by the sensing arrangement 12 during a modelingstep as described below. The data model generator 22 may extract, orcause the data analysis module 16 to extract, information from thecollected eye movement data that will constitute the threshold eyemovement data samples in the data model. The data model generator 22 maythen create the data model from the threshold eye movement data samples,and may store the data model in the model data store 20. In otherembodiments, the data model may be generated and stored in the modeldata store 20 by a separate modeling unit (not shown) of the system 10.The modeling unit may include its own sensing arrangement, processinghardware, and program modules. One suitable modeling unit may be theEyeLink 1000 by SR Research Ltd. of Mississauga, Ontario, Canada.

The control unit 14 may include or communicate with an alertingarrangement 24 configured to produce an alert to the user according tothe results of the data comparison in the comparison module 18. Thealerting arrangement may be any suitable indicator and associatedhardware and software for driving the indicator. Suitable indicatorsinclude, without limitation: a visual display such as one or morelight-emitting diodes, a liquid crystal display, a projector, and thelike; a bell, buzzer, or other audible signaling means; and apiezoelectric or other vibrating device.

The detection system 10 may be used to execute any suitable method ofdetecting dangerous conditions that are indicated by eye movement data.Referring to FIG. 2 , the detection system 10 may execute a method ofdetecting onset or presence of TBI in the user. At step 100, the systemmay record baseline measurements of the eye movement dynamics for thedata model. The baseline measurements are taken of a subject which mayor may not be the user. It may be advantageous that the data model usebaseline measurements of the user himself in order to individualize theoperation of the system, but the baseline measurements may be taken froma non-user subject, or taken from a plurality of subjects and averagedif desired. The conditions in which the baseline measurements arerecorded may depend on the desired specificity of the data model. Insome embodiments, the baseline measurements may be taken in normalconditions. In other embodiments, the baseline measurements may be takenin known injured conditions.

At step 105, the system may calculate one or more threshold driftvelocities from the recorded baseline measurements. The threshold driftvelocities may depend on the format of the collected baselinemeasurements. For example, where only normal-condition or onlyinjured-condition baseline measurements were taken, a single thresholddrift velocity (i.e., threshold-normal or threshold-TBI drift velocity)may be calculated. At step 110, the system may generate the data modelfor the baseline-tested subject(s). The data model may represent theprogression of the intersaccadic drift velocity of the subject fromnormal conditions to injured conditions, and further beyond a TBIthreshold into increasingly severe injury. The data model may begenerated and stored in any suitable format that allows the system tosubsequently compare eye movement data collected in situ from the useragainst the data model to determine the user's current impairment.

The steps 100, 105, 110 for obtaining the data model may be performed atany suitable time before testing the user in situ for signs of TBI. Inone embodiment, the steps 100-110 may be performed far in advance andremotely from the test environment. In another embodiment, the steps100-110 may be performed in the test environment, immediately precedingtesting the user. For example, the user may activate the system 10, suchas by donning and activating eyewear housing the sensing arrangement 12,which initiates step 100 of recording the baseline measurements in thepresent conditions. This may be in normal conditions, such as when theuser is about to drive his vehicle in the morning, and only the normaleye movement data would be collected as baseline measurements. In stillother embodiments, the data model may be created by the system 10 oranother system using a different method than described above.

At step 115, optionally the system may calibrate itself to the user ifthe data model or comparison method require it. For example, the datamodel may be a standardized model generated from baseline measurementsof (a) non-user subject(s), or the comparison method may determine thepresence of TBI from a percentage deviation from the user'sthreshold-normal drift velocity value(s). See below. In such anembodiment, the system calibrates (step 115) by recording a calibrationset, such as ten seconds or less but preferably five seconds or less, ofeye movement data of the user when the system is activated in the testenvironment under normal conditions. The system may compare thecalibration data to the data model. In one embodiment, this involvesdetermining a deviation of the user's threshold-normal drift velocityfrom the threshold-normal drift velocity of the model. The system canthen adapt the data model to the user.

At step 120, the system may record in situ eye movement data from theuser continuously or at predetermined intervals while the system isactivated. At step 125, the system may calculate, in real-time or atpredetermined intervals, the user's current drift velocity. At step 130,the system may compare the current drift velocity to the data model todetermine whether TBI has occurred. Such progression may be calculatedwithin any suitable paradigm. Examples include, without limitation:ratio or percentage by which the current drift velocity exceeds theuser's or the data model's threshold-normal drift velocity; ratio orpercentage by which the current drift velocity is below or above thethreshold-TBI drift velocity; comparison of current drift velocity topoints on a curve between threshold-normal and threshold-TBI values inthe data model; and the like. If the user is neither injured nor withina predetermined proximity to the threshold-TBI value of the data model,the system returns to step 120 and continues recording current data. Inone configuration, the system can be optionally interrupted fromcontinuing recording in situ measurements by an administrator orinstructions installed in the control unit. Such instructions can bestopping recording when a predetermined duration has reached, themeasurements are noisy or weak, or the administrator has entered a stopsignal. If the user's condition warrants (i.e., the current driftvelocity is above or within a certain range of the threshold-TBI value),at step 135 the system may alert the user to take corrective action.

In addition or alternatively to the methods described herein, the systemmay record and analyze eye movement data using any of the methods andsystem components described in copending U.S. patent application Ser.No. 14/220,265, co-owned by the present applicant and incorporated fullyherein by reference.

The described system and methods may be implemented in any environmentand during any task that may subject the user to dangerous conditionsthat affect eye movements. The various configurations presented aboveare merely examples and are in no way meant to limit the scope of thisdisclosure. Variations of the configurations described herein will beapparent to persons of ordinary skill in the art, such variations beingwithin the intended scope of the present application. In particular,features from one or more of the above-described configurations may beselected to create alternative configurations comprised of asub-combination of features that may not be explicitly described above.In addition, features from one or more of the above-describedconfigurations may be selected and combined to create alternativeconfigurations comprised of a combination of features which may not beexplicitly described above. Features suitable for such combinations andsub-combinations would be readily apparent to persons skilled in the artupon review of the present application as a whole. The subject matterdescribed herein and in the recited claims intends to cover and embraceall suitable changes in technology.

What is claimed is:
 1. A method of determining a traumatic brain injury(TBI) of a user, the method comprising: a) obtaining eye movement dataof one or both of the user's eyes; b) calculating, from the eye movementdata, one or more current intersaccadic drift velocities; c) comparingthe eye movement data, including the one or more current intersaccadicdrift velocities, to a data model including intersaccadic driftvelocities; and d) delivering an alert indicating results of thecomparison, including one of a presence, absence, progression, or aseverity of the TBI.
 2. The method of claim 1, wherein the data modelincludes a threshold intersaccadic drift velocity.
 3. The method ofclaim 1, wherein the data model includes a plurality of intersaccadicdrift velocities plotted along a curve ranging at least between athreshold-normal value to a threshold-TBI value.
 4. The method of claim1, wherein the data model includes baseline measurements of eye movementdynamics.
 5. The method of claim 4, wherein the baseline measurements ofeye movement dynamics are derived from previous eye movement data of theuser.
 6. The method of claim 4, wherein the baseline measurements of eyemovement dynamics are derived from eye movement data of a subject otherthan the user.
 7. The method of claim 4, wherein the baselinemeasurements of eye movement dynamics are derived from averaged eyemovement data of a plurality of subjects other than the user.
 8. Themethod of claim 4, wherein the baseline measurements of eye movementdynamics are derived from eye movement data of one of the user or asubject other than the user taken in normal conditions without a TBI. 9.The method of claim 4, wherein the baseline measurements of eye movementdynamics are derived from eye movement data of one of the user or asubject other than the user taken in conditions with a diagnosed TBI.10. The method of claim 1, wherein the data model represents aprogression of eye movement dynamics of the user from a “normal”condition to an “injury” condition.
 11. The method of claim 1 andfurther comprising calibrating the data model to the user prior to stepc).
 12. The method of claim 11, wherein calibrating the data modelincludes: obtaining a calibration set of eye movement data of one orboth of the user's eyes; comparing the calibration set of eye movementdata to the data model to determine a deviation of the calibration setof eye movement data from the data model; and adapting the data model tothe user based on the comparison.
 13. The method of claim 1, whereindelivering the alert includes providing a visual display of the results.14. The method of claim 1, wherein step a) further includes recordingthe eye movement data from one or both of the user's eyes.
 15. Themethod of claim 14 and further comprising continuing the recording untilone of the alert indicates the presence of the TBI, a predetermined timeduration has been reached, or a manual request to stop recording hasbeen received.
 16. The method of claim 1 and further comprisingdelivering a secondary alert to take corrective action when the resultsof the comparison indicate the eye movement data is one of above orwithin a certain range of a threshold-TBI value.
 17. A system fordetermining or monitoring a traumatic brain injury (TBI) of a user, thesystem comprising: a sensing arrangement configured to capture eyemovement data of the user; a control unit in communication with thesensing arrangement, the control unit being configured to: obtain theeye movement data from the sensing arrangement; calculate, from the eyemovement data, one or more current intersaccadic drift velocities;compare the eye movement data, including the one or more currentintersaccadic drift velocities, to a data model including intersaccadicdrift velocities; and deliver an alert indicating results of thecomparison, including one of a presence, absence, progression, or aseverity of a TBI.
 18. The system of claim 17, wherein the sensingarrangement is integrated into a handheld scanning device.
 19. Thesystem of claim 17, wherein the sensing arrangement is integrated intoeyewear configured to be worn by the user.
 20. The system of claim 17,wherein the control unit includes a data model generator configured tocreate the data model from prior eye movement data obtained from one ofthe user or one or more subjects other than the user.